Real-world data is messy, heterogeneous, and increasingly complex. Simultaneously, production systems must operate at scale with consistent performance. This paradox creates a significant challenge: how do we build sophisticated models that can handle complex data while maintaining production reliability? We present AWS's OSS advancements to bridge this gap by automating critical but time-consuming aspects of the ML pipeline. By providing low-code, easy-to-use frameworks that can handle tabular, graph, time series, and multi-modal data, we're democratizing access to sophisticated ML capabilities. This means businesses of all sizes - not just tech giants - can leverage ML for competitive advantage. In this talk, we will showcase state-of-the-art algorithms and research advancements, such as techniques for automatic graph construction from tabular data and efficient tabular model selection. Furthermore, we will share our recent approaches to push the boundaries of the AutoML domain with AutoGluon, including the integration of foundation models for improved time series forecasting and tabular data prediction, and an LLM-powered agent system for automated data science.